Autotrophic ammonia-oxidizing bacteria (AOB) are of vital importance to wastewater treatment plants (WWTP), as well as being an intriguing group of microorganisms in their own right. To date, corroboration of quantitative measurements of AOB by fluorescence in situ hybridization (FISH) has relied on assessment of the ammonia oxidation rate per cell, relative to published values for cultured AOB. Validation of cell counts on the basis of substrate transformation rates is problematic, however, because published cell-specific ammonia oxidation rates vary by over two orders of magnitude. We present a method that uses FISH in conjunction with confocal scanning laser microscopy to quantify AOB in WWTP, where AOB are typically observed as microcolonies. The method is comparatively simple, requiring neither detailed cell counts or image analysis, and yet it can give estimates of either cell numbers or biomass. Microcolony volume and diameter were found to have a log-normal distribution. We were able to show that virtually all (>96%) of the AOB biomass occurred as microcolonies. Counts of microcolony abundance and measurement of their diameter coupled with a calibration of microcolony dimensions against cell numbers or AOB biomass were used to determine AOB cell numbers and biomass in WWTP. Cell-specific ammonia oxidation rates varied between plants by over three orders of magnitude, suggesting that cell-specific ammonia oxidation is an important process variable. Moreover, when measured AOB biomass was compared with process-based estimates of AOB biomass, the two values were in agreement.The quantification of microbial communities and populations is an invaluable aspect of microbial ecology. In principle, the autotrophic ammonia-oxidizing bacteria (AOB) are ideal candidates for the development of quantitative tools. AOB have a coherent phylogeny and defined nutritional requirements and are of profound practical importance in natural and engineered environments.The number of individuals should be the ideal benchmark for quantitative studies. Individual counts can be converted to biomass, biovolume, or proportion of biomass, and results obtained by more indirect methods are typically compared to the number of cells per unit volume (15). Fluorescence in situ hybridization (FISH) represents the "gold standard" for quantification of specific bacterial cells in the environment, against which other methods should be compared. Classical (27) and immunological (20) methods are subject to methodological biases, while nonmicroscopic 16S rRNA-based methods (8, 34) or PCR-based methods (13,14,18,19) deliver a proportion of total cell counts, copy number, or relative signal intensities rather than an absolute number of cells or biomass.A quantitative method may be evaluated with respect to its precision and its accuracy. Wagner et al. (43) originally evaluated the accuracy of FISH counts of AOB by using cell specific oxidation rates, an approach previously used to show that most-probable-number-based methods underestimate AOB numbers...

Aims: The purpose of this work was to investigate microbial ecology of nitrifiers at the genus level in a typical full-scale activated sludge plant. Methods and Results: Grab samples of mixed liquor were collected from a plug-flow reactor receiving domestic wastewater. Fluorescent in situ hybridization technique (FISH) was used to characterize both ammonia oxidizing bacteria (AOB) and nitrite oxidizing bacteria (NOB) in combination with Confocal Scanning Laser Microscope (CSLM). Fluorescently labelled, 16S rRNA-targeted oligonucleotide probes were used in this study. Both Nitrosomonas and Nitrosospira genera as AOB and Nitrobacter and Nitrospira genera as NOB were sought with genus specific probes Nsm156, Nsv443 and NIT3 and NSR1156, respectively. Conclusions: It was shown that Nitrosospira genus was dominant in the activated sludge system studied, although Nitrosomonas is usually assumed to be the dominant genus. At the same time, Nitrobacter genus was detected in activated sludge samples. Significance and Impact of the Study: Previous studies based on laboratory scale pilot plants employing synthetic wastewater suggested that only Nitrospira are found in wastewater treatment plants. We have shown that Nitrobacter genus might also be present. We think that these kinds of studies may not give a valid indication of the microbial diversity of the real fullscale plants fed with domestic wastewater.

The COVID-19 pandemic triggered catastrophic impacts on human life, but at the same time demonstrated positive impacts on air quality. In this study, the impact of COVID-19 lockdown interventions on five major air pollutants during the pre-lockdown, lockdown, and post-lockdown periods is analysed in three urban areas in Northern England: Leeds, Sheffield, and Manchester. A Generalised Additive Model (GAM) was implemented to eliminate the effects of meteorological factors from air quality to understand the variations in air pollutant levels exclusively caused by reductions in emissions. Comparison of lockdown with pre-lockdown period exhibited noticeable reductions in concentrations of NO (56.68–74.16%), NO2 (18.06–47.15%), and NOx (35.81–56.52%) for measured data. However, PM10 and PM2.5 levels demonstrated positive gain during lockdown ranging from 21.96–62.00% and 36.24–80.31%, respectively. Comparison of lockdown period with the equivalent period in 2019 also showed reductions in air pollutant concentrations, ranging 43.31–69.75% for NO, 41.52–62.99% for NOx, 37.13–55.54% for NO2, 2.36–19.02% for PM10, and 29.93–40.26% for PM2.5. Back trajectory analysis was performed to show the air mass origin during the pre-lockdown and lockdown periods. Further, the analysis showed a positive association of mobility data with gaseous pollutants and a negative correlation with particulate matter.

Reliable prediction of municipal solid waste (MSW) generation rates is a significant element of planning and implementation of sustainable solid waste management strategies. In this study, the multi-layer perceptron artificial neural network (MLP-ANN) is applied to verify the prediction of annual generation rates of domestic, commercial and construction and demolition (C&D) wastes from the year 1997 to 2016 in Askar Landfill site in the Kingdom of Bahrain. The proposed robust predictive models incorporated selected explanatory variables to reflect the influence of social, demographical, economic, geographical and touristic factors upon waste generation rates (WGRs). The Mean Squared Error (MSE) and coefficient of determination ( R2) are used as performance indicators to evaluate effectiveness of the developed models. MLP-ANN models exhibited strong accuracy in predictions with high R2 and low MSE values. The R2 values for domestic, commercial and C&D wastes are 0.95, 0.99 and 0.91, respectively. Our results show that the developed MLP-ANN models are effective for the prediction of WGRs from different sources and could be considered as a cost-effective approach for planning integrated MSW management systems.

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